Discrete parameter Nonlinear Constrained Optimization of a Gear Train by Genetic Algorithms

Date

2005-01-01

Author

Dölen, Melik
Seıreg, Alı

Metadata

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This paper investigates the optimal design of a four-stage gear train using genetic algorithms. Five different genetic encoding schemes, which incorporate various heuristic search techniques, are proposed to deal with the most critical constraints of the problem. The fitness criterion used by all genetic algorithms includes a merit function for minimising the size of the gearbox. The results show improvement in the design merit over previous approaches without reliance on the designer's interaction to avoid geometric constraint violations and facilitate the convergence.

Subject Keywords

Genetic algorithms, Discrete design optimisation, Penalty function, Integer programming, Multi stage gear design, Monlinear programming, Gear train optimisation, Optimal design, Gearbox size, Geometric constraints

URI

https://hdl.handle.net/11511/75501
https://www.inderscienceonline.com/doi/abs/10.1504/IJCAT.2005.007213

Journal

INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY

Collections

Department of Mechanical Engineering, Article

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Citation Formats

M. Dölen and A. Seıreg, “Discrete parameter Nonlinear Constrained Optimization of a Gear Train by Genetic Algorithms,” INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY, pp. 110–121, 2005, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/75501.